复杂系统的代理建模与可解释人工智能：自动化仿真探索工作流
Surrogate Modeling and Explainable Artificial Intelligence for Complex Systems: A Workflow for Automated Simulation Exploration
AI总结：
针对复杂系统仿真驱动工作流的高计算成本与黑箱组件透明度不足问题，提出基于紧凑实验设计训练轻量级模拟器的工作流，结合XAI实现快速近似、不确定性量化与解释一致性评估，在飞机设计和城市隔离模型中验证其高效性与解释力。
摘要：
复杂系统正越来越多地通过融合物理模型、经验模型与优化及分析技术的仿真驱动工程工作流进行探索。尽管这些工作流功能强大，但面临两大核心障碍：（1）计算成本高昂，因为精准探索需要大量昂贵的仿真器运行；（2）当决策依赖不透明的黑箱组件时，透明度和可靠性有限。我们提出一种工作流，通过在紧凑实验设计上训练轻量级模拟器来应对这两个挑战：（i）提供昂贵仿真器的快速、低延迟近似；（ii）实现严格的不确定性量化；（iii）适配全局和局部可解释人工智能（XAI）分析。该工作流统一了所有基于仿真的复杂系统分析工具，从工程设计到用于社会环境理解的基于智能体的模型。本文提出了在该工作流中使用基于代理的可解释性工具的比较方法和实践建议，支持连续和分类输入，将全局效应与不确定性分析与局部归因相结合，评估代理模型间解释的一致性，从而诊断代理的充分性并指导进一步的数据收集或模型优化。我们在两个对比案例研究中验证了该方法：混合电动飞机的多学科设计分析和城市隔离的基于智能体的模型。结果表明，代理模型与XAI的耦合可在几秒内实现大规模探索，揭示非线性相互作用和涌现行为，识别关键设计和政策杠杆，并标记代理需要更多数据或替代架构的区域。
英文摘要：
Complex systems are increasingly explored through simulation-driven engineering workflows that combine physics-based and empirical models with optimization and analytics. Despite their power, these workflows face two central obstacles: (1) high computational cost, since accurate exploration requires many expensive simulator runs; and (2) limited transparency and reliability when decisions rely on opaque blackbox components. We propose a workflow that addresses both challenges by training lightweight emulators on compact designs of experiments that (i) provide fast, low-latency approximations of expensive simulators, (ii) enable rigorous uncertainty quantification, and (iii) are adapted for global and local Explainable Artificial Intelligence (XAI) analyses. This workflow unifies every simulation-based complex-system analysis tool, ranging from engineering design to agent-based models for socio-environmental understanding. In this paper, we proposea comparative methodology and practical recommendations for using surrogate-based explainability tools within the proposed workflow. The methodology supports continuous and categorical inputs, combines global-effect and uncertainty analyses with local attribution, and evaluates the consistency of explanations across surrogate models, thereby diagnosing surrogate adequacy and guiding further data collection or model refinement. We demonstrate the approach on two contrasting case studies: a multidisciplinary design analysis of a hybrid-electric aircraft and an agent-based model of urban segregation. Results show that the surrogate model and XAI coupling enables large-scale exploration in seconds, uncovers nonlinear interactions and emergent behaviors, identifies key design and policy levers, and signals regions where surrogates require more data or alternative architectures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
